Uncertainty Shocks in a Model of Effective Demand

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1 Uncertainty Shocks in a Model of Effective Demand Susanto Basu Brent Bundick Abstract Can increased uncertainty about the future cause a contraction in output and its components? This paper examines uncertainty shocks in a one-sector, representativeagent general-equilibrium model. When prices are flexible, uncertainty shocks cannot produce business-cycle comovements among key macroeconomic variables. However, uncertainty shocks can generate business-cycle fluctuations with countercyclical markups through sticky prices. If the central bank is constrained by the zero lower bound, higher uncertainty has even more negative effects on the economy. We calibrate our model using fluctuations in the VIX and find that increased uncertainty about the future may have played a role in worsening the Great Recession. JEL Classification: E32, E52 Keywords: Uncertainty Shocks, Monetary Policy, Sticky-Price Models, Zero Lower Bound on Nominal Interest Rates We thank Robert Barro, Nick Bloom, David Chapman, Fabio Ghironi, Liam Graham, Cosmin Ilut, Taisuke Nakata, Julio Rotemberg and Christina Wang for helpful discussions, and seminar participants at the Federal Reserve Bank of Boston, Boston College, the conference on Labor Market Institutions and the Macroeconomy, the European Central Bank, the 211 SCE Conference on Computing in Economics and Finance, the 211 NBER Summer Institute, the Boston University and Boston Fed Conference on Macro-Financial Linkages, the Philadelphia Fed and NBER Workshop on Methods and Applications of DSGE Models, the Fall 211 NBER Monetary Economics Program Meeting, Columbia University, the Federal Reserve Bank of New York, the Central Bank of Chile, and the Federal Reserve Board for comments. Boston College and NBER. basusd@bc.edu Boston College. bundickb@bc.edu 1

2 1 Introduction Economists and the financial press often discuss uncertainty about the future as an important driver of economic fluctuations, and a contributor in the Great Recession and subsequent slow recovery. For example, Diamond (21) says, What s critical right now is not the functioning of the labor market, but the limits on the demand for labor coming from the great caution on the side of both consumers and firms because of the great uncertainty of what s going to happen next. Recent research by Bloom (29), Bloom, Foetotto, Jaimovich, Saporta-Eksten, and Terry (211), Fernàndez-Villaverde, Guerròn- Quintana, Kuester, and Rubio-Ramìrez (211), Born and Pfeifer (211), and Gilchrist, Sim, and Zakrajšek (21) also suggests that uncertainty shocks can cause fluctuations in macroeconomic aggregates. However, most of these papers experience difficulty in generating business-cycle comovements among output, consumption, investment, and hours worked from changes in uncertainty. If uncertainty is a contributing factor in the Great Recession and persistently slow recovery, then increased uncertainty should reduce output and its components. In this paper, we show why competitive, one-sector, closed-economy models generally cannot generate business-cycle comovements in response to changes in uncertainty. Under reasonable assumptions, an increase in uncertainty about the future induces precautionary saving and lower consumption. If households supply labor inelastically, then total output remains constant since the level of technology and capital stock remain unchanged in response to the uncertainty shock. Unchanged total output and reduced consumption together imply that investment must rise. If households can adjust their labor supply and consumption and leisure are both normal goods, an increase in uncertainty also induces precautionary labor supply, or a desire for the household to supply more labor for an given level of the real wage. As current technology and the capital stock remain unchanged, the competitive demand for labor remains unchanged as well. Thus, higher uncertainty reduces consumption but raises output, investment, and hours worked. This lack of comovement is a robust prediction of simple neoclassical models subject to uncertainty fluctuations. We also show that non-competitive, one-sector models with countercyclical markups through sticky prices can easily overcome the comovement problem and generate simultaneous drops in output, consumption, investment, and hours worked in response 2

3 to an uncertainty shock. An increase in uncertainty induces precautionary labor supply by the representative household, which reduces firm marginal costs of production. Falling marginal costs with slowly-adjusting prices imply an increase in firm markups over marginal cost. A higher markup reduces the demand for consumption, and especially, investment goods. Since output is demand-determined in these models, output and employment must fall when consumption and investment both decline. Thus, comovement is restored, and uncertainty shocks cause fluctuations that look qualitatively like a business cycle. Returning to Diamond s (21) intuition, simple competitive business-cycle models do not exhibit movements in the demand for labor as a result of an uncertainty shock. However, uncertainty shocks easily cause fluctuations in the demand for labor in non-competitive, sticky-price models with endogenously-varying markups. Thus, the non-competitive model captures the intuition articulated by Diamond. Understanding the dynamics of the demand for labor explains why the two models behave so differently in response to a change in uncertainty. Importantly, the non-competitive model is able to match the estimated effects of uncertainty shocks in the data by Bloom (29) and Alexopoulos and Cohen (29), while the competitive model cannot. To analyze the quantitative impact of uncertainty shocks under flexible and sticky prices, we calibrate and solve a representative-agent, dynamic stochastic general equilibrium model with nominal price rigidity. We examine uncertainty shocks to both technology and household discount factors, which we interpret as cost and demand uncertainty. We calibrate our uncertainty shock processes using the Chicago Board Options Exchange Volatility Index (VIX), which measures the expected volatility of the Standard and Poor s 5 stock index over the next thirty days. Using a third-order approximation to the policy functions of our calibrated model, we show that uncertainty shocks can produce contractions in output and all its components when prices adjust slowly. In particular, we find that increased uncertainty associated with future demand can produce significant declines in output, hours, consumption, and investment. Our model predicts that a one standard deviation increase in the uncertainty about future demand produces a peak decline in output of about.2 percent. Finally, we examine the role of monetary policy in determining the equilibrium effects of uncertainty shocks. Standard monetary policy rules imply that the central bank usually offsets increases in uncertainty by lowering its nominal policy rate. We show that increases in uncertainty have larger negative impacts on the economy if the monetary authority is 3

4 constrained by the zero lower bound on nominal interest rates. In these circumstances, our model predicts that an increase in uncertainty causes a much larger decline in output and its components. The sharp increase in uncertainty during the financial crisis in late 28 corresponds to a period when the Federal Reserve had a policy rate near zero. Thus, we believe that greater uncertainty may have plausibly contributed significantly to the large and persistent output decline starting at that time. Our results suggest that about one-fourth of the drop in output that occurred in late 28 can plausibly be ascribed to increased uncertainty about the future. Our emphasis on the effects of uncertainty in a one-sector model does not mean that we deprecate alternative modeling strategies. For example, Bloom, Foetotto, Jaimovich, Saporta-Eksten, and Terry (211) examine changes in uncertainty in a heterogeneousfirm model with convex and non-convex adjustment costs. However, this complex model is unable to generate positive comovement of the four key macro aggregates following an uncertainty shock. Furthermore, heterogeneous-agent models are challenging technically to extend along other dimensions. For example, adding nominal price rigidity for each firm and a zero lower bound constraint on nominal interest rates would be difficult in the model of Bloom, Foetotto, Jaimovich, Saporta-Eksten, and Terry (211). We view our work as a complementary approach to modeling the business-cycle effects of uncertainty. The simplicity of our underlying framework allows us to tackle additional issues that we think are important for understanding the Great Recession. 2 Intuition This section formalizes the intuition from the introduction using a few key equations that characterize a large class of one-sector business cycle models. We show that the causal ordering of these equations plays an important role in understanding the impact of uncertainty shocks. These equations link total output Y t, household consumption C t, investment I t, hours worked N t, and the real wage W t /P t. The following key equations consist of a demand equation, an aggregate production function, and a static first-order condition for a representative consumer to maximize utility: Y t = C t + I t, (1) 4

5 Y t = F (K t, Z t N t ), (2) W t P t U 1 (C t, 1 N t ) = U 2 (C t, 1 N t ). (3) Typical partial-equilibrium results suggest that an increase in uncertainty about the future decreases both consumption and investment. When consumers face a stochastic income stream, higher uncertainty about the future induces precautionary saving by riskaverse households. Recent work by Bloom (29) argues that an increase in uncertainty also depresses investment, particularly in the presence of non-convex costs of adjustment. If an increase in uncertainty lowers consumption and investment in partial equilibrium, Equation (1) suggests that it should lower total output in a general-equilibrium model. In a setting where output is demand-determined, economic intuition suggests that higher uncertainty should depress total output and its components. However, the previous intuition is incorrect in a general-equilibrium neoclassical model with a representative firm and a consumer with additively time-separable preferences. In this neoclassical setting, labor demand (the partial derivative of Equation (2) with respect to N t ) is determined by the current level of capital and technology, neither of which changes when uncertainty increases. The first-order conditions for firm labor demand derived from Equation (2) and the labor supply condition in Equation (3) can be combined to yield: Z t F 2 (K t, Z t N t )U 1 (C t, 1 N t ) = U 2 (C t, 1 N t ). (4) Equation (4) defines a positively-sloped income expansion path for consumption and leisure for given levels of capital and technology. If higher uncertainty reduces consumption, then Equation (4) shows that increased uncertainty must increase labor supply. However, Equation (2) implies that total output must rise. A reduction in consumption and an increase in total output in Equation (1) means that investment and consumption must move in opposite directions. 1 In a non-neoclassical setting, especially one with a time-varying markup of price over marginal cost, Equations (1) and (3) continue to apply, but Equation (4) must be modified, and becomes: 1 µ t Z t F 2 (K t, Z t N t )U 1 (C t, 1 N t ) = U 2 (C t, 1 N t ) (5) 1 This argument follows Barro and King (1984). Jaimovich (28) shows that this prediction may not hold for certain classes of preferences that are not additively time-separable. 5

6 where µ t is the markup of price over marginal cost. In such a setting, Equation (1) is causally prior to Equations (2) and (3). From Equation (1), output is determined by aggregate demand. Equation (2) then determines the necessary quantity of labor input for given values of K t and Z t. Finally, given C t (determined by demand and other factors), the necessary supply of labor is made consistent with consumer optimization by having the markup taking on its required value. Alternatively, the wage moves to the level necessary for firms to hire the required quantity of labor, and the variable markup ensures that the wage can move independently of the marginal product of labor. The previous intuition can also be represented graphically using simplified labor supply and labor demand curves in real wage and hours worked space. Figures 1 and 2 show the impact of an increase in uncertainty under both flexible prices with constant markups and sticky prices with endogenously-varying markups. An increase in uncertainty induces wealth effects on the representative household through the forward-looking marginal utility of wealth denoted by λ t. An increase in the marginal utility of wealth shifts the household labor supply curve outward. With flexible prices and constant markups, the labor demand curve remains fixed for a given level of the real wage. In the flexible-price equilibrium, the desire of households to supply more labor translates into higher equilibrium hours worked and a lower real wage. When prices adjust slowly to changing marginal costs, however, firm markups over marginal cost rise when the household increases their labor supply. For a given level of the real wage, an increase in markups decreases the demand for labor from firms. Figure 2 shows that equilibrium hours worked may fall as a result of the outward shift in the labor supply curve and the inward shift of the labor demand curve. The relative magnitudes of the changes in labor supply and labor demand depend on the specifics of the macroeconomic model and its parameter values. The following section shows that in a reasonably calibrated New-Keynesian sticky price model, firm markups increase enough to produce a decrease in equilibrium hours worked in response to an increase in uncertainty. 3 Model This section outlines the baseline dynamic stochastic general equilibrium model that we use in our analysis of uncertainty shocks. Our model provides a specific quantitative 6

7 example of the intuition of the previous section. The baseline model shares many features with the models of Ireland (23), Ireland (211), and Jermann (1998). The model features optimizing households and firms and a central bank that systematically adjusts the nominal interest rate to offset adverse shocks in the economy. We allow for sticky prices using the quadratic-adjustment costs specification of Rotemberg (1982). Our baseline model considers both technology shocks and household discount rate shocks. Both shocks have time-varying second moments, which have the interpretation of cost uncertainty and demand uncertainty. 3.1 Households In our model, the representative household maximizes lifetime utility given Epstein-Zin preferences over streams of consumption, C t, and leisure, 1 N t. The household solves its optimization problem subject to its risk aversion over the consumption-leisure basket σ and its intertemporal elasticity of substitution ψ. The parameter θ V (1 σ) (1 1/ψ) 1 controls the household s preference for the resolution of uncertainty. 2 The household receives labor income W t for each unit of labor N t supplied in the representative intermediate goods-producing firm. The representative household also owns the intermediate goods firm and holds equity shares S t and one-period riskless bonds B t issued by representative intermediate goods firm. Equity shares pay dividends Dt E for each share S t owned, and the riskless bonds return the gross one-period risk-free interest rate Rt R. The household divides its income from labor and its financial assets between consumption C t and the amount of financial assets S t+1 and B t+1 to carry into next period. The discount rate of the household β is subject to shocks via the stochastic process a t. Since our model is a standard dynamic general-equilibrium model without government, any non-technological source of shocks must come from changes in preferences. Therefore, we interpret changes in the household discount factor as demand shocks hitting the economy. The representative household maximizes lifetime utility by choosing C t+s, N t+s, B t+s+1, and S t+s+1 for all s =, 1, 2,... by solving the following problem: [ ( 1 σ V t = max a t C η t (1 N t ) 1 η) θ V ( ) ] + β Et Vt+1 1 σ 1 θ V 1 σ θ V 2 Our main results are robust to using expected utility preferences over consumption and leisure. The use of Epstein-Zin preferences allows us to calibrate our model using stock market data. Section 6.1 explains the details of our calibration method. 7

8 subject to its intertemporal household budget constraint each period, C t + P t E S t P t Rt R B t+1 W ( t D E N t + t P t P t + P ) t E S t + B t. P t Using a Lagrangian approach, household optimization implies the following first-order conditions: P E t P t = E t V t C t = λ t (6) V t W t = λ t (7) N t P t ) ( D E t+1 + P )} t+1 E (8) P t+1 P t+1 )} (9) {( β λ t+1 λ t 1 = R R t E t {( β λ t+1 λ t where λ t denotes the Lagrange multiplier on the household budget constraint. The utility function specification implies the following stochastic discount factor M t+1 : ( ) Vt / C t+1 M t+1 = V t / C t ( β a t+1 a t ) ( C η t+1 (1 N t+1 ) 1 η C η t (1 N t ) 1 η ) 1 σ θ V ( Ct C t+1 ) ( V [ t+1 ] E t V 1 σ t+1 ) 1 1 θ V Using the stochastic discount factor, we can eliminate λ and simplify Equations (7) - (9) as follows: P E t P t 1 η C t = W t η 1 N t P t (1) { ( D E = E t M t+1 t+1 + P )} t+1 E P t+1 P t+1 (11) { } 1 = Rt R E t Mt+1 (12) Equation (1) represents the household intratemporal optimality condition with respect to consumption and leisure, and Equations (11) and (12) represent the Euler equations for equity shares and one-period riskless firm bonds. 8

9 3.2 Intermediate Goods Producers Each intermediate goods-producing firm i rents labor N t (i) from the representative household to produce intermediate good Y t (i). Intermediate goods are produced in a monopolistically competitive market where producers face a quadratic cost of changing their nominal price P t (i) each period. The intermediate-goods firms own the capital stock K t (i) for the economy and face adjustment costs for adjusting its rate of investment. Each firm issues equity shares S t (i) and one-period risk-less bonds B t (i). Firm i chooses N t (i), I t (i), and P t (i) to maximize firm cash flows D t (i)/p t (i) given aggregate demand Y t and price P t of the finished goods sector. The intermediate goods firms all have the same constant returns-to-scale Cobb-Douglas production function, subject to a fixed cost of production Φ. Each intermediate goods-producing firm maximizes discounted cash flows using the household stochastic discount factor: max E t subject to the production function: s= [ ] Dt+s (i) M t+s P t+s [ ] θµ Pt (i) Y t K t (i) α [Z t N t (i)] 1 α Φ, and subject to the capital accumulation equation: where D t (i) P t = K t+1 (i) = [ Pt (i) P t P t ( 1 δ φ K 2 ( ) ) 2 It (i) K t (i) δ K t (i) + I t (i) ] 1 θµ Y t W t N t (i) I t (i) φ P P t 2 [ ] 2 Pt (i) ΠP t 1 (i) 1 Y t The behavior of each firm i satisfies the following first-order conditions: W t P t N t (i) = (1 α)ξ t K t (i) α [Z t N t (i)] 1 α (13) 9

10 q t = E t { [ ] [ Pt (i) φ P ΠP t 1 (i) 1 M t+1 ( R K t R K t+1 + q t+1 ( K t (i) = αξ t K t (i) α [Z t N t (i)] 1 α (14) P t ] [ ] θµ [ ] θµ 1 P t Pt (i) Pt (i) = (1 θ µ ) + θ µ Ξ t ΠP t 1 (i) P t P ] [ t ]} (15) Pt+1 (i) +φ P E t { M t+1 Y t+1 1 δ φ K 2 Y t [ Pt+1 (i) ΠP t (i) 1 ( ) 2 ( It+1 It+1 δ + φ K δ K t+1 K t+1 ( ) 1 It = 1 φ K δ q t K t P t ΠP t (i) P t (i) ) ( ) ))} It+1 K t+1 where Ξ t is the marginal cost of producing one additional unit of intermediate good i, and q t is the price of a marginal unit of installed capital. R K t /P t is the marginal revenue product of capital, which is paid to the owners of the capital stock. Our adjustment cost specification is similar to the specification used by Jermann (1998) and Ireland (23), and allows Tobin s q to vary over time. Each intermediate goods firm finances a percentage ν of its capital stock each period with one-period riskless bonds. The bonds pay the one-period real risk-free interest rate. Thus, the quantity of bonds B t (i) = νk t (i). Total firm cash flows are divided between payments to bond holders and equity holders as follows: D E t (i) P t = D t(i) P t ν ( K t (i) 1 R R t (16) (17) ) K t+1 (i). (18) Since the Modigliani and Miller (1958) theorem holds in our model, leverage does not affect firm value or optimal firm decisions. Leverage makes the payouts and price of equity more volatile and allows us to define a concept of equity returns in the model. We use the volatility of equity returns implied by the model to calibrate our uncertainty shock processes in Section Final Goods Producers The representative final goods producer uses Y t (i) units of each intermediate good produced by the intermediate goods-producing firm i [, 1]. The intermediate output is 1

11 transformed into final output Y t using the following constant returns to scale technology: [ 1 ] θµ Y t (i) θµ 1 θµ 1 θµ di Each intermediate good Y t (i) sells at nominal price P t (i) and each final good sells at nominal price P t. The finished goods producer chooses Y t and Y t (i) for all i [, 1] to maximize the following expression of firm profits: P t Y t 1 Y t P t (i)y t (i)di subject to the constant returns to scale production function. Finished goods-producer optimization results in the following first-order condition: [ ] θµ Pt (i) Y t (i) = Y t P t The market for final goods is perfectly competitive, and thus the final goods-producing firm earns zero profits in equilibrium. Using the zero-profit condition, the first-order condition for profit maximization, and the firm objective function, the aggregate price index P t can be written as follows: 3.4 Monetary Policy [ 1 P t = ] 1 1 θµ P t (i) 1 θµ di We assume a cashless economy where the monetary authority sets the net nominal interest rate r t to stabilize inflation and output growth. Monetary policy adjusts the nominal interest rate in accordance with the following rule: r t = ρ r r t 1 + (1 ρ r ) (r + ρ π (π t π) + ρ y y t ), (19) where r t = ln(r t ), π t = ln(π t ), and y t = ln(y t /Y t 1 ). Changes in the nominal interest rate affect expected inflation and the real interest through the Fisher relation ln(r t ) = ln(e t Π t+1 ) + ln(r R t ). Thus, we include the following Euler equation for a zero net supply 11

12 nominal bond in our equilibrium conditions: {( )} Mt+1 1 = R t E t Π t+1 (2) 3.5 Equilibrium The assumption of Rotemberg (1982) (as opposed to Calvo (1983)) pricing implies that we can model our production sector as a single representative intermediate goods-producing firm. In the symmetric equilibrium, all intermediate goods firms choose the same price P t (i) = P t, employ the same amount of labor N t (i) = N t, and choose to hold the same amount of capital K t (i) = K t. Thus, all firms have the same cash flows and payout structure between bonds and equity. With a representative firm, we can define the unique markup of price over marginal cost as µ t = 1/Ξ t, and gross inflation as Π t = P t /P t Shock Processes In our baseline model, we are interested in capturing the effects of independent changes in the level and volatility of both the technology process and the preference shock process. The technology and preference shock processes are parameterized as follows: Z t = (1 ρ z ) Z + ρ z (Z t 1 ) + σ z t ε z t σ z t = (1 ρ σ z) σ z + ρ σ zσ z t 1 + σ σz ε σz t a t = (1 ρ a ) a + ρ a a t 1 + σ a t ε a t σ a t = (1 ρ σ a) σ a + ρ σ aσ a t 1 + σ σa ε σa t ε z t and ε a t are first moment shocks that capture innovations to the level of the stochastic processes for technology and household discount factors. We refer to ε σz t and ε σz t as second moment or uncertainty shocks since they capture innovations to the volatility of the exogenous processes of the model. An increase in the volatility of the shock process increases the uncertainty about the future time path of the stochastic process. All four stochastic shocks are independent, standard normal random variables. 12

13 3.7 Solution Method Our primary focus of this paper is to examine the effects of increases in the second moments of the shock processes. Using a standard first-order or log-linear approximation to the equilibrium conditions of our model would not allow us to examine second moment shocks, since the approximated policy functions are invariant to the volatility of the shock processes. Similarly, second moment shocks would only enter as cross-products with the other state variables in a second-order approximation to the policy functions, and thus we could not study the effects of shocks to the second moments alone. In a third-order approximation, however, second moment shocks enter independently in the approximated policy functions. Thus, a third-order approximation allows us to compute an impulse response to an increase in the volatility of technology or discount rate shocks, while holding constant the levels of those variables. To solve the baseline model, we use the Perturbation AIM algorithm and software developed by Swanson, Anderson, and Levin (26). Perturbation AIM uses Mathematica to compute the rational expectations solution to the model using nth-order Taylor series approximation around the nonstochastic steady state of the model. We find that a third-order approximation to the policy functions is sufficient to capture the dynamics of the baseline model. As discussed in Fernàndez-Villaverde, Guerròn-Quintana, Rubio- Ramìrez, and Uribe (211), approximations higher than first-order move the ergodic distributions of the endogenous variables of the model away from their deterministic steadystate values. In the following analysis, we compute the impulse responses in percent deviation from the ergodic mean of each model variable. 4 Calibration and Baseline Results 4.1 Calibration Table 1 lists the calibrated parameters of the model. We calibrate the model at a quarterly frequency, using standard parameters for one-sector models of fluctuations. Since our model shares many features with the estimated models of Ireland (23) and Ireland (211), we calibrate our model to match the estimated parameters reported in those papers. We use the estimates in these papers to calibrate the steady-state volatilities for the technology and preference shocks, σ z and σ a. We calibrate the steady-state level 13

14 of the discount factor and technology processes a and Z to both equal one. To assist in numerically calibrating and solving the model, we introduce constants into the period utility function and the production function to normalize the value function V and output Y to both equal one at the deterministic steady state. We choose steady-state hours worked N and the model-implied value for η such that our model has a Frisch labor supply elasticity of 1. Our calibration of φ K implies an elasticity of the investmentcapital ratio with respect to marginal q of 2.. The household IES is calibrated to.5, which is consistent with the empirical estimates of Basu and Kimball (22). The fixed cost of production for the intermediate-goods firm Φ is calibrated to eliminate pure profits in the deterministic steady state of the model. Risk aversion over the consumption and leisure basket σ is set to 6, which is inline with the estimated values of van Binsbergen, Fernàndez-Villaverde, Koijen, and Rubio-Ramìrez (21) and Swanson and Rudebusch (212). We discuss our calibration of the uncertainty shock stochastic processes in depth in Section 6. In the following analysis, we compare the results from our baseline stickyprice calibration (φ P = 16) with a flexible-price calibration (φ P = ). 4.2 Uncertainty Shocks & Business Cycle Comovements Holding the calibrated parameters fixed, we analyze the effects of an exogenous increase in uncertainty associated with technology or household demand. Figure 3 plots the impulse responses of the model to a technology uncertainty shock and Figure 4 plots the responses to a demand uncertainty shock. The results are consistent with the intuition of Section 2 and the labor market diagrams in Figures 1 and 2. Uncertainty from either technology or household demand both enter Equation (4) or Equation (5) through the forward-looking marginal utility of wealth. An uncertainty shock associated with either stochastic process induces wealth effects on the household which triggers precautionary labor supply. Thus, the responses and time paths for the endogenous variables look qualitatively similar for both types of uncertainty shocks. Households want to consume less and save more when uncertainty increases in the economy. In order to save more, households optimally wish to both reduce consumption and increase hours worked. Under flexible prices and constant markups, equilibrium labor supply and consumption follow the path that households desire when they face higher uncertainty. On impact of the uncertainty shock, the level of capital is predetermined, 14

15 the level of the shock process is held constant, and thus labor demand is unchanged for a given real wage. Under flexible prices, the outward shift in labor supply combined with unchanged labor demand increases hours worked and output. After the impact period, households continue to save, consume less, and work more hours. Since firms owns the capital stock, higher household saving translates into higher capital accumulation for firms. Throughout the life of the uncertainty shock, consumption and investment move in opposite directions, which is inconsistent with basic business-cycle comovements. Under sticky prices, households also want to consume less and save more when the economy is hit by an uncertainty shock associated with technology or household demand. On impact, households increase their labor supply and reduce consumption to accumulate more assets. With sticky prices, however, increased labor supply decreases the marginal costs of production of the intermediate goods firms. A reduction in marginal cost with slowly-adjusting prices increases firm markups. An increase in markups lowers the demand for household labor and lowers the real wage earned by the representative household. The decrease in labor demand also lowers investment in the capital stock by firms. In equilibrium, these effects combine to produce significant falls in output, consumption, investment, hours worked, and the real wage, which are consistent with business-cycle facts. Thus, the desire by households to work more can actually lead to lower labor input and output in equilibrium. 5 Discussion and Connections 5.1 Specific Example of General Principle The differential response of our economy under flexible and sticky prices to uncertainty fluctuations is a specific instance of the general proposition established by Basu and Kimball (25). They show that good shocks that cause output to rise in a flexible-price model generally tend to have contractionary effects in a model with nominal price rigidity. Basu and Kimball (25) also show that the response of monetary policy is critical for determining the equilibrium response of output and other variables. If monetary policy follows a sensible rule, for example the celebrated Taylor (1993) rule, then the monetary authority typically lowers the nominal interest rate to offset the negative short-run effects of the shock. Our results show, however, this effect is not strong enough for standard 15

16 parameter values. Even though the monetary authority in our model lowers interest rates when uncertainty rises, it does not succeed in offsetting the contractionary effects of uncertainty with nominal rigidities. In keeping with the bulk of the literature, we do not model why the monetary policy rule does not react more aggressively to uncertainty in normal times. However, we do investigate in depth one particular barrier to expansionary monetary policy that is critical for understanding the Great Recession: the zero lower bound constraint on nominal interest rates. If uncertainty increases when the monetary authority is unable to lower the nominal interest rate further because the policy rate is essentially zero, as was the case in late 28 and early 29, then the short-run contractionary effect of the good shock dominates, and the equilibrium response of output becomes robustly negative. We explore this issue in Section Extension to Sticky Nominal Wages Our exposition so far suggests that the mechanism we have identified works only in the special case where nominal prices are sticky but wages are flexible. Indeed, our intuition for the channel through which an increase in uncertainty raises the markup has emphasized these two elements. We argued that higher uncertainty induces households to work at lower wages, the reduction in the wage reduces firms marginal costs, but since their output prices are fixed, lower marginal costs translate to higher markups, which are contractionary. However, various types of evidence suggests that nominal wages are sticky, not flexible, especially at high frequencies. At the macro level, Christiano, Eichenbaum, and Evans (25) find that nominal wage stickiness is actually more important than nominal price stickiness for explaining the observed impact of monetary policy shocks. At the micro level, Barattieri, Basu, and Gottschalk (21) find that the wages of individual workers are often unchanged for long periods of time (with wages changed, on average, less than once a year). In this subsection, we show that our results extend readily to the case where either or both nominal prices and wages are sticky. Rather than writing down an extended model with two nominal frictions, we make our point heuristically, using the graphical labor supply-labor demand apparatus of Section 2. As we argued above, if households act competitively in the labor market: U 2 (C t, 1 N t ) = λ t W t, (21) 16

17 where W is the nominal wage and λ is the shadow value of nominal wealth (the utility value of the marginal dollar). implies that Thus, Assuming firms have market power, cost-minimization W t = P t µ P t U 2 (C t, 1 N t ) λ t P t = 1 µ P t Z t F 2 (K t, Z t N t ). (22) Z t F 2 (K t, Z t N t ), (23) where µ P t is the price-markup over marginal cost. Now assume a new model, where households also have market power, and set wages with a markup over their marginal disutility of work: W t = µ W t U 2 (C t, 1 N t ) λ t (24) Then, U 2 (C t, 1 N t ) λ t P t = 1 µ W t 1 µ P t Z t F 2 (K t, Z t N t ) (25) In our labor market diagrams, suppose we replace the labor supply curve with U 2 (C t, 1 N t )/λ t P t. This quantity has the interpretation of being the disutility faced by the household of supplying one more unit of labor, expressed in units of real goods (the real marginal cost of supplying labor). On the vertical axis, put the equilibrium level of the real marginal disutility of work. Note that this supply curve is shifted in exactly the same way by uncertainty as the standard labor supply curve of Figures 1 and 2 higher uncertainty raises λ, which shifts the supply curve out. But now the demand curve (the right-hand side of (25)) is shifted by both price and wage markups only the product of the two matters. Take the polar opposite of the case we have analyzed so far: Assume perfect competition in product markets, but Rotemberg wage setting by monopolistically competitive households in the labor market. Then the price markup is always fixed at 1, but the wage markup would jump up in response to an increase in uncertainty (since the marginal cost of supplying labor falls but the wage is sticky), making the qualitative outcome exactly the same as in our current case with only sticky prices and flexible wages. Thus, while introducing nominal wage stickiness would certainly affect quantitative magnitudes, it would not change our qualitative results. 17

18 5.3 Connections with Existing Literature Our framework can be used to understand the economic mechanisms at work in some recent papers in the literature. Recent work by Bloom, Foetotto, Jaimovich, Saporta- Eksten, and Terry (211), Chugh (21), and Gilchrist, Sim, and Zakrajšek (21) uses flexible-price models to show that shocks to uncertainty can lead to fluctuations that resemble business cycles. Their modeling approach is to drop Equation (2) and use multisector models of production. Follow the insight of Bloom (29), the normal industry equilibrium in these models features resource reallocation from low- to high-productivity firms. Higher uncertainty impedes the reallocation process by reducing the necessary investment or disinvestment needed to move capital and labor to higher-productivity uses. These models use multi-sector production and costly factor adjustment to transform a change in the expected future dispersion of total factor productivity (TFP) into a change in the current mean of the TFP distribution. 3 This approach may allow equilibrium real wages, consumption and labor supply to move in the same direction. However, all three papers experience difficulties in getting the desired comovements, at least for calibrations that are consistent with steady-state growth. We view these approaches are complementary to ours since both mechanisms (cyclical markups and cyclical reallocation) could be at work simultaneously. However, we view our approach as a realistic and tractable alternative, since non-linear heterogeneous-agent models are computationally difficult to analyze. Our model of time-varying markups allows us to analyze uncertainty in the same representative-agent DSGE framework used to study other real and monetary shocks. A recent paper by Fernàndez-Villaverde, Guerròn-Quintana, Rubio-Ramìrez, and Uribe (211) studies the effects of uncertainty in a small open economy setting, where they directly shock the exogenous process for the real interest rate. Since a small open economy analysis is effectively done in a partial-equilibrium framework, they experience no difficulties in getting business-cycle comovements from an uncertainty shock. As we show, 3 This intuition also helps understand the recent work of Bidder and Smith (212), which embeds stochastic volatility and preferences for robustness in a business-cycle model. In their setting, an increase in volatility of technology shocks affects the expected mean of the technology distribution by changing the conditional worst case distribution of the robustness-seeking agent. In a related paper, Ilut and Schneider (211) embed ambiguity-averse agents in the model of Smets and Wouters (27). They show that exogenous changes in the agents beliefs about the worst-case scenario can produce business-cycle comovements. 18

19 the difficulties come when the real interest rate is endogenous in a general equilibrium framework. In this setting, our mechanism changes the qualitative predictions of baseline DSGE models, and makes the model predictions consistent with the empirical evidence. Another recent paper by Gourio (21) follows Rietz (1988) and Barro (26) and introduces a time-varying disaster risk into an otherwise-standard real business cycle. This shock can be viewed as bad news about the future first moment of technology combined with an increase in the future dispersion of technology. Thus, a higher risk of disaster is a combination of a negative news shock and a shock that increases uncertainty about the future. However, a key difference between Gourio (21) and our work is that a realized disaster affects the level of both technology and the capital stock. In our model, a realized innovation does not affect the level of capital at the impact of the shock. The additional assumption in Gourio (21) implies that an increase in the probability of disaster directly lowers the risk-adjusted rate of return on capital. In order for investment to fall when the probability of disaster increases, Gourio must assume an intertemporal elasticity of substitution (IES) greater than one. With an IES greater than one, the substitution effect dominates the wealth effect when the probability of disaster increases. The lower risk-adjusted rate of return on investment induces the household to decrease investment. Since the return on investment is low, households supply less labor which lowers total output. Since leisure and consumption are normal goods, an increase in risk results in lower equilibrium output, investment, and hours, but higher equilibrium consumption. For the reasons we discuss in Section 2, his competitive onesector model is unable to match basic business-cycle comovements. A key difference is that our mechanism is able to generate business-cycle comovement with any calibrated value for the IES. In independent and simultaneous work, papers by Fernàndez-Villaverde, Guerròn- Quintana, Kuester, and Rubio-Ramìrez (211) and Born and Pfeifer (211) examine the role of fiscal uncertainty shocks in a model with nominal wage and price rigidities. Fernàndez-Villaverde, Guerròn-Quintana, Kuester, and Rubio-Ramìrez (211) shows that uncertainty regarding future fiscal policy is transmitted to the macroeconomy primarily through uncertainty about future taxes on income from capital. As we discuss in the introduction, an increase in uncertainty with nominal rigidities changes markups and creates macroeconomic comovement. We view this work as highly complementary to our paper. Our work emphasizes the basic mechanism in a stripped-down model and shows why fluc- 19

20 tuations in uncertainty can create business cycle comovement. These two papers show that the mechanism we identify can have important economic effects in the benchmark medium-scale model of Smets and Wouters (27). Other than sharing a mechanism for generating comovement, these two papers differ greatly from our work. We focus on technology and demand uncertainty, rather than policy uncertainty. In addition, we follow a very different calibration strategy, which we discuss in the next section. The object of our paper is to understand the role of increased uncertainty in generating the Great Recession and the subsequent slow recovery. We also analyze the interaction between the zero lower bound on nominal interest rates and uncertainty shocks, which we view as important for understanding the economics of this period. 6 Quantitative Results and the Great Recession 6.1 Uncertainty Shock Calibration The intuition laid out in Sections 1 and 2, and the previous qualitative results suggest that uncertainty shocks can produce declines in output and its components when prices adjust slowly. This section uses the previous sticky-price model to determine if uncertainty shocks are quantitatively important for business cycle fluctuations. A related issue is determining the proper calibration of our shock processes for the uncertainty shocks associated with technology and household demand. The transmission of uncertainty to the macroeconomy in our model crucially depends on the calibration of the size and persistence of the uncertainty shock processes. However, aggregate uncertainty shocks are an ex ante concept, which may be difficult to measure using ex post economic data. To ensure our calibration of an unobservable process is reasonable, we want our model and uncertainty shock processes to be consistent with a well-known and observable measure of aggregate uncertainty. We choose the Chicago Board Options Exchange Volatility Index (VIX) as our observable measure of aggregate uncertainty due to its prevalence in financial markets, ease of observability, and the ability to generate a model counterpart. The VIX is a forwardlooking indicator of the expected volatility of the Standard and Poor s 5 stock index. To match the frequency of our model, we aggregate an end-of-month VIX series to quarterly frequency by averaging over the three months in each quarter. The top panel of Figure 5 2

21 plots our quarterly VIX series. Using our VIX data series, denoted Vt D, we estimate the following simple reduced-form autoregressive time series model: ln(vt D ) = (1 ρ V )ln(v D ) + ρ V ln(vt 1) D + σ V D ε V D t, ε V D t N(, 1). (26) The ordinary least squares regression results are V D = 2.4%, ρ V =.83, and σ V D =.19 with an R 2 =.68. Using the estimated parameters, we can also compute a series of VIXimplied uncertainty shocks as the regression residuals divided by the sample standard deviation. Compared to its sample average of 2.4%, a one standard deviation VIXimplied uncertainty shock raises the level of the VIX to 24.27%. The bottom plot of Figure 5 shows the time series of the VIX-implied uncertainty shocks. We use this reducedform time-series model to ensure a reasonable calibration for our technology and demand uncertainty shocks processes. We want to create a model concept that is the counterpart to our observable measure of aggregate uncertainty. Therefore, we compute a model-implied VIX index as the expected conditional volatility of the return on the equity of the representative intermediate-goods producing firm. Using the third-order approximation to the policy functions of the model, we define our model-implied VIX V M t V M t = 1 as follows: 4 VAR t ( R E t+1 ), (27) where VAR t (R E t+1) is the quarterly conditional variance of the equity return. 4 We annualize the quarterly conditional variance, and then transform the annual volatility units into percentage points. Using our model-implied VIX, we calibrate leverage and the uncertainty shock parameters using a two-step process. Given the other parameters for the model and the unconditional shock variances σ a and σ z, we first choose the level of firm leverage such that the unconditional level of the model-implied VIX at the ergodic mean matches the average level of the VIX in the data, 2.4 percent. 5 After matching the unconditional level of the model-implied VIX, we then choose our uncertainty shock parameters such 4 Technically, the VIX is the expected volatility of equity returns under the risk-neutral measure. In the model, the results are quantitatively unchanged if we compute the model-implied VIX using the risk-neutral expectation. 5 Since the Modigliani & Miller (1963) theorem holds in our model, the amount of leverage does not affect firm decisions or firm value. 21

22 that a one standard deviation uncertainty shock in our model, to either technology or household demand, generates an impulse response that closely matches our reduced-form estimate for the actual VIX in the data. For example, in our calibrated model a one standard deviation uncertainty shock to technology or household demand produces a 19 percent increase in the model-implied VIX and has a first-order autoregressive term of.83. Conditional on the values of the endogenous state variables, our model-implied VIX has an AR(1) representation in each of the two types of uncertainty shocks. Therefore, we are able to closely match the impulse response of the simple reduced-form model. 6.2 Quantitative Impact of Uncertainty Shocks Figure 6 shows the impact of our calibrated uncertainty shock processes on the endogenous variables of the sticky-price model. Section 4.2 shows that the responses are qualitatively similar for both technology and household demand uncertainty shocks. In this section, we analyze the quantitative differences between technology and household demand uncertainty shocks. The bottom right plot of Figure 6 shows that both uncertainty shocks under sticky prices produce a similar law of motion in the model-implied VIX, which approximately matches the reduced-form VIX model. The bottom middle plot of each figure shows that the percentage increase in the volatility of the exogenous shocks to generate the same movement in the model-implied VIX differs between technology and household demand shocks. Household preference shocks require a 96 percent increase in volatility to produce the same movement in the model-implied VIX as a 37 percent increase in the volatility of technology. In addition, the quantitative transmission of uncertainty to the macroeconomy differs greatly between the technology and household demand shocks. A one standard deviation technology uncertainty shock generates a peak drop in output of less than.5 percent. However, a one standard deviation household demand uncertainty shock produces a peak drop in output of about.17 percent. Much of the quantitative difference in the output fluctuations originates from the behavior of investment. When the uncertainty about future technology increases, higher capital provides a hedge against possible negative shocks to future marginal costs. This additional substitution effect, which is not present under a demand uncertainty shock, provides an incentive for the firm to not disinvest in the capital stock when uncertainty about future technology increases. Accordingly, 22

23 investment falls by only a few basis points after a technology uncertainty shock but falls by over 2 basis points after a demand uncertainty shock. Since capital and labor are complements in production, the time path of investment implies that equilibrium hours worked also falls by less after a technology uncertainty shock. Overall, our results suggest that household demand uncertainty shocks can cause quantitatively significant fluctuations in output and its components. Our calibration strategy produces general-equilibrium results which are consistent with the empirical literature on the macroeconomic effects of stock market volatility. Alexopoulos and Cohen (29) analyze the effects of stock market volatility on industrial production using a vector autoregression with a recursive identification scheme. They show that a one standard deviation increase in the VIX produces a statistically significant decline of output with a peak decline of approximately.25 percent. Our calibrated impulse responses of demand uncertainty shocks are close to this point estimate and well within its confidence interval, which provides additional evidence that our calibration strategy is reasonable. 6.3 The Role of Uncertainty Shocks in the Great Recession The previous section shows that uncertainty shocks associated with household demand have quantitatively significant effects on output and its components. Many economists and the financial press believe the large increase in uncertainty in the fall of 28 may have played a role in the Great Recession and subsequent slow recovery. 6 The plot of the VIX in Figure 5 shows a large increase in expected stock market volatility around the collapse of Lehman Brothers in September of 28. In particular, the bottom plot shows a three and a half standard deviation VIX-implied uncertainty shock during the end of 28. In calibrating our model, one standard deviation uncertainty shocks to either household demand or technology generate one standard deviation movements in the model-implied VIX. Thus, we cannot easily identify or partition the contribution of demand or technology uncertainty shocks in our model in generating the large change in the VIX in the fall of 28. However, the utilization-adjusted total factor productivity series of Fernald (211) shows very little evidence of stochastic volatility, either during the Great Recession or over the entire postwar period. Thus, if we assume demand uncertainty shocks explain 6 For example, Kocherlakota (21) states, I ve been emphasizing uncertainties in the labor market. More generally, I believe that overall uncertainty is a large drag on the economic recovery. 23